Five Steps to Make IoT Great Again

September 5, 2016

The Internet of Things (IoT) is the next great wave of change in our technology and society. Any world leader or business executive should be heavily focused on making IoT great for their constituents and customers. Whether you are Donald Trump with a presidential bid or you’re a developer at a promising startup — when you’re ready, here are the fundamental steps to get your solutions started.

1. Secure Connectivity — Before any sensor can send data to a cloud it must first be connected. The connection from that sensor must bounce along multiple protocols (from ZigBee to MQTT to REST). The sensor must authenticate itself and then be granted permissions only for valid activities. Finally the device must be managed and maintained so that it continues to provide valuable information rather than slowly becoming insecure and a point of frustration for our users. Failure to implement secure connectivity results in data breaches, unsatisfied users, and wasted investments.

2. Real-time processing — Once devices are connected and able to communicate securely, the next challenge of real-time processing jumps to the foreground. IoT will see demand greater for volumes of information and higher speeds than any of the previous systems of the past. To simply digest the ingress, parse the data payloads, execute rules and provide responses, IoT solution creators will have to work in new ways. This includes the ability to create horizontally scalable infrastructure, optimized data transfers, and flexible deployment models which is not currently standard in our architectures. Before data can be processed we must first simply make the connected devices become actionable in real-time.

3. Dynamic Data Routing — Only at step three do we begin to engage our data needs. With the ability to process the fire hose of connected device data, we now need to flow and enrich that data into valuable locations. There are many unique data stores that will provide different value, from highly available caches to legacy relational databases to domain specific Hadoop clusters, each will most likely exist in your IoT solution. At this point simply routing the data into the correct locations for future access and processing is required.

4. Deep Analysis and Learning — Only once our IoT data resides in the correct place can we begin to do analysis. Before many AI engines can begin to answer questions they must be trained. This training is done via vast amounts of softly correlated event data or with experts seeding the systems. Here we can leverage intelligence engines provided by IBM Watson, Google Big Query, and Microsoft AI.

5. Cognitive Decisions — Finally we can mature our IoT solutions to the last phase of implementation by introducing our intelligent networks back into our IoT real-time processing. This means that rather than a simple rule checking for temperature greater than 120 that issues an alert, we can pass in an array of sensor values that include ambient temp, machine humidity, vibration, weather forecast, upcoming procurement outline, project sales demands all with a high percentage of certainty to select an ideal maintenance window in our factories.

The future with IoT is very bright as we make our societies safer, efficient and aware. As you and your enterprise explore leveraging IoT, make sure to consider the true path and costs of achieving your first steps before jumping to the final result.

A final note — The good news is that the big data, AI, neural networks, and expert engines are all in their infancy. Solving the core issues of connectivity and real time processing first means that you will be ready when those tools finally mature.

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